class_docstr: Mu-law decoding (expansion) of mu-law encoded audio signal to [-1, 1].
  See `Wikipedia <https://en.wikipedia.org/wiki/Μ-law_algorithm>`_ for more details.
  
  Args:
      quantization_channels (positive int): Number of channels. For 8-bit encoding, use 256.
      **kwargs: optional keyword args for `tf.keras.layers.Layer()`
  
  Example:
      ::
  
          input_shape = (2048, 1)  # mono signal (int in [0, quantization_channels - 1])
          model = Sequential()
          model.add(kapre.MuLawDecoding(quantization_channels=256, input_shape=input_shape))
          # now the shape is (batch, time=2048, ch=1) with float dtype in [-1, 1]
docstr: Args:
      x (int `Tensor`): audio signal to decode. Shape doesn't matter.
  
  Returns:
      (float `Tensor`): mu-law encoded x. Shape doesn't change.
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code: def call(self, x):
      """
  
      Args:
          x (int `Tensor`): audio signal to decode. Shape doesn't matter.
  
      Returns:
          (float `Tensor`): mu-law encoded x. Shape doesn't change.
      """
      return backend.mu_law_decoding(x, self.quantization_channels)
  
imports: import tensorflow
  from tensorflow.keras.layers import Layer
  from tensorflow.keras import backend
  from .None import backend
  from .backend import _CH_FIRST_STR, _CH_LAST_STR, _CH_DEFAULT_STR
label_desc: Use image node_id 2 for calling the MuLawDecoding obj's underlying member method code with example usage: obj.call(x) and returns: backend